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Traditional deep neural networks (NNs) have significantly
contributed to the state-of-the-art performance in the task of
classification under various application domains. However,
NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly
model the uncertainty of class probabilities and use them for
classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the
ENN is trained as a black box without explicitly considering
inherent uncertainty in data with their different root causes,
such as vacuity (i.e., uncertainty due to a lack of evidence) or
dissonance (i.e., uncertainty due to conflicting evidence). By
considering the multidimensional uncertainty, we proposed
a novel uncertainty-aware evidential NN called WGAN-ENN
(WENN) for solving an out-of-distribution (OOD) detection
problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain
class, which has high vacuity for OOD samples. Via extensive
empirical experiments based on both synthetic and real-world
datasets, we demonstrated that the estimation of uncertainty
by WENN can significantly help distinguish OOD samples
from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts
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